Visual robot navigation in outdoor environments would benefit from an illumination-independent representation of images. We explore how such a representation, comprising a black skyline of objects in front of a white sky, can be obtained from dual-channel spectral contrast measures. Light from sky and natural objects under different conditions of illumination was analyzed by five spectral channels: ultraviolet, blue, green, red, and near infrared. Linear discriminant analysis was applied to determine the optimal linear separation between sky and object points. A statistical comparison shows that contrasts with large differences in the wavelength of the two channels, specifically ultraviolet-infrared, blue-infrared, and ultraviolet-red, yield the best separation. Within a single channel, the best separation was obtained for ultraviolet light. The gain in separation quality when all five channels were included is relatively small.